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Biology subjects

Shao, P.

Publications and source records attributed to Shao, P..

7 recordsLinked to original sources

Functional orthogonality of WRN inhibitor resistance enables alternating therapy and mutation-tolerant inhibitor design in MSI-H cancers

Werner syndrome helicase (WRN) is a synthetic-lethal vulnerability in microsatellite instability-high (MSI-H)/mismatch repair-deficient (dMMR) cancers1-5, and non-covalent and covalent WRN inhibitors are now entering clinical development6-9. A central unresolved question is whether resistance to one WRN inhibitor class inevitably compromises the target, or instead creates actionable vulnerabilities to an alternate modality. Here we generated ten stable acquired-resistance models across three MSI-H cell lines using the non-covalent inhibitor HRO761 and the covalent inhibitor VVD-214. Whole-exome sequencing, biochemical reconstitution and isogenic knock-in models identified recurrent on-target WRN missense mutations as dominant resistance drivers, but with sharply modality-specific spectra: HRO761 resistance clustered at G729/F730/I852, whereas VVD-214 resistance concentrated at E846. These mutations impaired inhibitor engagement and abolished the canonical WRN inhibitor (WRNi)-induced pharmacodynamic cascade, including WRN reduction, DNA damage response (DDR) activation and G2/M arrest. Crucially, most resistance mutations retained biochemical, cellular and in vivo sensitivity to the alternate inhibitor modality, revealing a functional orthogonality that enabled a 7-day cyclic alternating regimen to delay tumour regrowth in xenografts. We further identified F730L as an engineered cross-resistant bottleneck model and used structure-guided, artificial intelligence (AI)-enabled optimization to generate GBA-007, a proof-of-concept mutation-tolerant WRN inhibitor candidate with promising activity against F730L in vitro and in vivo. Thus, clinically relevant WRN inhibitor classes impose distinct on-target resistance trajectories that can be exploited through schedule design, while cross-resistant bottlenecks can be addressed by rapid mutation-aware inhibitor engineering.

Cancer Biology↗

Advancing Ligand Binding Affinity Prediction with Cartesian Tensor-Based Deep Learning

0.We present PBCNet2.0, a cartesian tensor-based Siamese Neural Network for protein-ligand relative binding affinity prediction. Trained on 8.6 million protein-ligand complex structure pairs, PBCNet2.0 achieves zero-shot performance comparable to computationally intensive physics-based simulations. Our prioritization experiments show that PBCNet2.0 speeds up binding affinity optimization by 718% while reducing resource use by 41%. Through extensive retrospective experiments, we demonstrate that PBCNet2.0 intrinsically comprehends protein-ligand interactions, showing high sensitivity to intermolecular interactions and exceptional perception of spatial geometric information. Strikingly, PBCNet2.0 exhibits an emergent capability to predict affinity changes induced by binding residue variations, highlighting its potential for identifying resistance mutation. We prospectively validated these capabilities on two targets ENPP1 and ALDH1B1, where PBCNet2.0 successfully identified affinity shifts arising from subtle molecular interactions and conformational differences, and pinpointed critical binding residues with an 83% hit rate. This combination of computational efficiency, spatial geometric perception of binding site, and generalizable affinity prediction establishes PBCNet2.0 as a transformative tool for developing pharmacological probes for all human proteins.

molecular biology↗

GhCPK28 negatively regulates the immune response by phosphorylating GhTIFY3b

The soil-borne fungus Verticillium dahliae (V. dahliae) seriously inhibits cotton (Gossypium hirsutum) growth and productivity. The immune system of cotton against this pathogen is largely unknown. Here we investigated the involvement of Ca2+-dependent protein kinases (CPKs) in this immunity. One CPK coding gene GhCPK28 had reduced expression after infection by V. dahliae in cotton. Knocking down of the GhCPK28 by virus induced gene silencing led to enhanced resistance to V. dahliae which is accompanied by a higher ROS accumulation and systemic acquired response. GhCPK28 was found to phosphorylate a TIFY family protein GhTIFY3b and reduce its stability. Silencing GhTIFY3b increased the susceptibility of cotton to V. dahliae. Together, this study indicate that GhCPK28 is a negative regulator of defense against V. dahliae infection and the GhTIFY3b might be a target protein of its activity. It sheds light on the immune system against V. dahliae and provides candidate genes for improving resistance against Verticillium wilt in cotton.

plant biology↗

Intermediate basal cell population in prostate homeostasis and cancer initiation

Many glandular epithelia are mainly composed of basal cells and luminal cells, including the prostate gland. Adult prostate basal and luminal cells are independently self-sustained by unipotent stem cells that can reactivate multipotency under prostate inflammation and carcinogenesis contexts. However, the defined basal stem cell populations responsible for prostate regeneration and their cell fates in prostate homeostasis, inflammation and carcinogenesis remain unclear. Using a genetic proliferation tracer (ProTracer) system, we found that basal cells exhibited extensive cell loss and proliferation during androgen-mediated prostate regression and regeneration, respectively. A rare intermediate basal cell population that expresses luminal cell markers (Nkx3.1 and Pbsn) (termed Basal-B) and a large basal cell population (termed Basal-A) were identified in mouse prostates by single-cell RNA sequencing. Basal-B cells exhibited a greater capacity for organoid formation and luminal cell differentiation in vitro. Genetic lineage tracing using dual recombinases showed that prostate homeostasis and regeneration are not driven by specific basal cell types. Fate-mapping results showed that Basal-B cells had a greater tendency to generate luminal cells under bacteria-induced prostate inflammation. Deletion of Pten in basal cells resulted in Basal-A-to-Basal-B-to-luminal transition and prostatic intraepithelial neoplasia. Moreover, the human Basal-B-cell population was significantly increased in human benign prostate hyperplasia and prostatic intraepithelial neoplasia samples compared with normal prostate samples. This study identifies intermediate Basal-B cells as a potential stem cell population and provides genetic evidence of prostate basal cell lineage plasticity under physiological and pathological contexts.

developmental biology↗

Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model

BackgroundThe 2019 new coronavirus, "2019-nCoV", was discovered from Wuhan Viral Pneumonia cases in December 2019, and was named by the World Health Organization on January 12, 2020. In the early stage, people knows little about the 2019-nCoV virus was not clear, and the spread period was encountering Chinas annual spring migration, which made the epidemic spread rapidly from Wuhan to almost all provinces in China. MethodsThis study builds a SEIRD model that considers the movement of people across regions, revealing the effects of three measures on controlling the spread of the epidemic.Based on MATLAB R2017a, computational experiments were performed to simulate the epidemic prevention and control measures. FindingsThe research results show that current prevention and control measures in China are very necessary. This study further validates the concerns of international and domestic experts regarding asymptomatic transmission (E-status). InterpretationThe results of this study are applicable to explore the impact of the implementation of relevant measures on the prevention and control of epidemic spread, and to identify key individuals that may exist during the spread of the epidemic.

microbiology↗

Synergistic interplay between PHF8 and HER2 signaling contributes to breast cancer development and drug resistance

HER2 plays a critical role in tumorigenesis and is associated with poor prognosis of HER2-positive breast cancers. Although, anti-HER2 drugs show benefits in breast cancer therapy, de novo or acquired resistance often develop. Epigenetic factors have been increasingly targeted for therapeutic purposes, however, such mechanisms interacting with HER2 signaling are poorly understood. This study reports the synergistic interplay between histone demethylase PHF8 and HER2 signaling, i.e. PHF8 is elevated in HER2-positive breast cancers and is upregulated by HER2; PHF8 plays coactivator roles in regulating HER2 expression and HER2-driven epithelial-to-mesenchymal transition (EMT) markers and cytokines. The HER2-PHF8-IL-6 regulatory axis was proved both in cell lines and in the newly established MMTV-Her2/MMTV-Cre/Phf8flox/flox models, with which the oncogenic function of Phf8 in breast cancer in vivo was revealed for the first time. Furthermore, PHF8-IL-6 axis contributes to the resistance of Trastuzumab in vitro and may play a critical role in the infiltration of T-cells in HER2-driven breast cancers. This study reveals novel epigenetic mechanisms underlying HER2-driven cancer development and anti-HER2 drug resistance.

cancer biology↗

Modeling and Visualization of Rice Root Based on Morphological Parameters

To clarify the morphological distributional characteristic of rice roots, the "root box" experiments are conducted to extract various morphological parameters of roots. On the basis of experiments, in this paper, the rice root model based on morphological parameters is constructed with B-spline curves by analyzing the topological structure of rice roots, quantifying their biological characteristics, summarizing the morphological structure and growth characteristics and improving the Cubic growth function to describe the growth change of rice roots. Meanwhile, the output accuracy of the model is tested. Finally, the dynamic simulation of rice root growth characteristics in three-dimensional space is implemented by using Visual C++ and OpenGL standard graphics library. The compared results demonstrate that the model could faithfully simulate the dynamic growing process of rice roots, and help to enrich the methods of digitization and visualization for roots of other crops.

bioinformatics↗